Python PIL.Image.AFFINE Examples

def scale_rotate__translate(image, angle, center=None, new_center=None, scale=None, resample=Image.BICUBIC):
    if (scale is None) and (center is None):
        return image.rotate(angle=angle, resample=resample)
    nx, ny = x, y = center
    sx = sy = 1.0
    if new_center:
        (nx, ny) = new_center
    if scale:
        (sx, sy) = (scale, scale)
    cosine = math.cos(angle)
    sine = math.sin(angle)
    a = cosine / sx
    b = sine / sx
    c = x - nx * a - ny * b
    d = -sine / sy
    e = cosine / sy
    f = y - nx * d - ny * e
    return image.transform(image.size, Image.AFFINE, (a, b, c, d, e, f), resample=resample) 

def ScaleRotateTranslate(self, image, angle, center=None, new_center=None,
                             scale=None, expand=False):
        '''
        experimental - not used yet
        '''
        if center is None:
            return image.rotate(angle, expand)
        angle = -angle / 180.0 * math.pi
        nx, ny = x, y = center
        if new_center != center:
            (nx, ny) = new_center
        sx = sy = 1.0
        if scale:
            (sx, sy) = scale
        cosine = math.cos(angle)
        sine = math.sin(angle)
        a = cosine / sx
        b = sine / sx
        c = x - nx * a - ny * b
        d = -sine / sy
        e = cosine / sy
        f = y - nx * d - ny * e
        return image.transform(image.size, Image.AFFINE,
                               (a,b,c,d,e,f), resample=Image.BICUBIC) 

def _shear_y_impl(pil_img, level):
  """Applies PIL ShearY to `pil_img`.

  The ShearY operation shears the image along the vertical axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, 0, level, 1, 0)) 

def _shear_x_impl(pil_img, level):
  """Applies PIL ShearX to `pil_img`.

  The ShearX operation shears the image along the horizontal axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, level, 0, 0, 1, 0)) 

def translate_x(pil_img, level):
    level = int_parameter(sample_level(level), IMAGE_SIZE / 3)
    if np.random.random() > 0.5:
        level = -level
    return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                            Image.AFFINE, (1, 0, level, 0, 1, 0),
                            resample=Image.BILINEAR) 

def _translate_x_impl(pil_img, level):
  """Applies PIL TranslateX to `pil_img`.

  Translate the image in the horizontal direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateX applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, level, 0, 1, 0)) 

def _translate_y_impl(pil_img, level):
  """Applies PIL TranslateY to `pil_img`.

  Translate the image in the vertical direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateY applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, 0, 0, 1, level)) 

def _translate_y_impl(pil_img, level, img_shape):
  """Applies PIL TranslateY to `pil_img`.

  Translate the image in the vertical direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateY applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform(
      _width_height_from_img_shape(img_shape),
      Image.AFFINE,
      (1, 0, 0, 0, 1, level)) 

def _translate_x_impl(pil_img, level, img_shape):
  """Applies PIL TranslateX to `pil_img`.

  Translate the image in the horizontal direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateX applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform(
      _width_height_from_img_shape(img_shape),
      Image.AFFINE,
      (1, 0, level, 0, 1, 0)) 

def _shear_y_impl(pil_img, level, img_shape):
  """Applies PIL ShearY to `pil_img`.

  The ShearY operation shears the image along the vertical axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform(
      _width_height_from_img_shape(img_shape),
      Image.AFFINE,
      (1, 0, 0, level, 1, 0)) 

def _shear_x_impl(pil_img, level, img_shape):
  """Applies PIL ShearX to `pil_img`.

  The ShearX operation shears the image along the horizontal axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform(
      _width_height_from_img_shape(img_shape),
      Image.AFFINE,
      (1, level, 0, 0, 1, 0)) 

def ScaleRotateTranslate(image, angle, center=None, new_center=None, scale=None, resample=Image.BICUBIC):
        if (scale is None) and (center is None):
            return image.rotate(angle=angle, resample=resample)
        nx, ny = x, y = center
        sx = sy = 1.0
        if new_center:
            (nx, ny) = new_center
        if scale:
            (sx, sy) = (scale, scale)
        cosine = math.cos(angle)
        sine = math.sin(angle)
        a = cosine / sx
        b = sine / sx
        c = x - nx * a - ny * b
        d = -sine / sy
        e = cosine / sy
        f = y - nx * d - ny * e
        return image.transform(image.size, Image.AFFINE, (a, b, c, d, e, f), resample=resample)
        # 根据所给的人脸图像，眼睛坐标位置，偏移比例，输出的大小，来进行裁剪。 

def ScaleRotateTranslate(image, angle, center=None, new_center=None, scale=None, resample=Image.BICUBIC):
        if (scale is None) and (center is None):
            return image.rotate(angle=angle, resample=resample)
        nx, ny = x, y = center
        sx = sy = 1.0
        if new_center:
            (nx, ny) = new_center
        if scale:
            (sx, sy) = (scale, scale)
        cosine = math.cos(angle)
        sine = math.sin(angle)
        a = cosine / sx
        b = sine / sx
        c = x - nx * a - ny * b
        d = -sine / sy
        e = cosine / sy
        f = y - nx * d - ny * e
        return image.transform(image.size, Image.AFFINE, (a, b, c, d, e, f), resample=resample)
        # 根据所给的人脸图像，眼睛坐标位置，偏移比例，输出的大小，来进行裁剪。 

def ScaleRotateTranslate(image, angle, center=None, new_center=None, scale=None, resample=Image.BICUBIC):
        if (scale is None) and (center is None):
            return image.rotate(angle=angle, resample=resample)
        nx, ny = x, y = center
        sx = sy = 1.0
        if new_center:
            (nx, ny) = new_center
        if scale:
            (sx, sy) = (scale, scale)
        cosine = math.cos(angle)
        sine = math.sin(angle)
        a = cosine / sx
        b = sine / sx
        c = x - nx * a - ny * b
        d = -sine / sy
        e = cosine / sy
        f = y - nx * d - ny * e
        return image.transform(image.size, Image.AFFINE, (a, b, c, d, e, f), resample=resample)
        # 根据所给的人脸图像，眼睛坐标位置，偏移比例，输出的大小，来进行裁剪。 

def ScaleRotateTranslate(image, angle, center=None, new_center=None, scale=None, resample=Image.BICUBIC):
        if (scale is None) and (center is None):
            return image.rotate(angle=angle, resample=resample)
        nx, ny = x, y = center
        sx = sy = 1.0
        if new_center:
            (nx, ny) = new_center
        if scale:
            (sx, sy) = (scale, scale)
        cosine = math.cos(angle)
        sine = math.sin(angle)
        a = cosine / sx
        b = sine / sx
        c = x - nx * a - ny * b
        d = -sine / sy
        e = cosine / sy
        f = y - nx * d - ny * e
        return image.transform(image.size, Image.AFFINE, (a, b, c, d, e, f), resample=resample)
        # 根据所给的人脸图像，眼睛坐标位置，偏移比例，输出的大小，来进行裁剪。 

def _translate_y_impl(pil_img, level):
  """Applies PIL TranslateY to `pil_img`.

  Translate the image in the vertical direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateY applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, 0, 0, 1, level)) 

def _translate_x_impl(pil_img, level):
  """Applies PIL TranslateX to `pil_img`.

  Translate the image in the horizontal direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateX applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, level, 0, 1, 0)) 

def _shear_y_impl(pil_img, level):
  """Applies PIL ShearY to `pil_img`.

  The ShearY operation shears the image along the vertical axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, 0, level, 1, 0)) 

def _shear_x_impl(pil_img, level):
  """Applies PIL ShearX to `pil_img`.

  The ShearX operation shears the image along the horizontal axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, level, 0, 0, 1, 0)) 

def _translate_y_impl(pil_img, level):
  """Applies PIL TranslateY to `pil_img`.

  Translate the image in the vertical direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateY applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, 0, 0, 1, level)) 

def _translate_x_impl(pil_img, level):
  """Applies PIL TranslateX to `pil_img`.

  Translate the image in the horizontal direction by `level`
  number of pixels.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had TranslateX applied to it.
  """
  level = int_parameter(level, 10)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, level, 0, 1, 0)) 

def ScaleRotateTranslate(self, image, angle, center=None, new_center=None,
                             scale=None, expand=False):
        '''
        experimental - not used yet
        '''
        if center is None:
            return image.rotate(angle, expand)
        angle = -angle / 180.0 * math.pi
        nx, ny = x, y = center
        if new_center != center:
            (nx, ny) = new_center
        sx = sy = 1.0
        if scale:
            (sx, sy) = scale
        cosine = math.cos(angle)
        sine = math.sin(angle)
        a = cosine / sx
        b = sine / sx
        c = x - nx * a - ny * b
        d = -sine / sy
        e = cosine / sy
        f = y - nx * d - ny * e
        return image.transform(image.size, Image.AFFINE,
                               (a,b,c,d,e,f), resample=Image.BICUBIC) 

def _shear_y_impl(pil_img, level):
  """Applies PIL ShearY to `pil_img`.

  The ShearY operation shears the image along the vertical axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, 0, 0, level, 1, 0)) 

def _shear_x_impl(pil_img, level):
  """Applies PIL ShearX to `pil_img`.

  The ShearX operation shears the image along the horizontal axis with `level`
  magnitude.

  Args:
    pil_img: Image in PIL object.
    level: Strength of the operation specified as an Integer from
      [0, `PARAMETER_MAX`].

  Returns:
    A PIL Image that has had ShearX applied to it.
  """
  level = float_parameter(level, 0.3)
  if random.random() > 0.5:
    level = -level
  return pil_img.transform((32, 32), Image.AFFINE, (1, level, 0, 0, 1, 0)) 

def prepare_char(some_char, angle=20, shear=10, scale=2):
    phi = np.radians(np.random.uniform(-angle,angle))
    theta = np.radians(np.random.uniform(-shear,shear))
    a = scale**np.random.uniform(-1,1)
    b = scale**np.random.uniform(-1,1)
    (x,y) = some_char.size
    x = a*x
    y = b*y
    xextremes = [rot_x(phi,theta,0,0),rot_x(phi,theta,0,y),rot_x(phi,theta,x,0),rot_x(phi,theta,x,y)]
    yextremes = [rot_y(phi,theta,0,0),rot_y(phi,theta,0,y),rot_y(phi,theta,x,0),rot_y(phi,theta,x,y)]
    mnx = min(xextremes)
    mxx = max(xextremes)
    mny = min(yextremes)
    mxy = max(yextremes)

    aff_bas = np.array([[a*np.cos(phi+theta), b*np.sin(phi-theta), -mnx],[-a*np.sin(phi+theta), b*np.cos(phi-theta), -mny],[0, 0, 1]])
    aff_prm = np.linalg.inv(aff_bas)
    some_char = some_char.transform((int(mxx-mnx),int(mxy-mny)), 
                                  method = Image.AFFINE, 
                                  data = np.ndarray.flatten(aff_prm[0:2,:]))
    some_char = some_char.resize((int(32*(mxx-mnx)/105),int(32*(mxy-mny)/105)))
    
    return some_char

# Crop scaled images to character size 

def translate_x(pil_img, level):
  level = int_parameter(sample_level(level), IMAGE_SIZE / 3)
  if np.random.random() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, level, 0, 1, 0),
                           resample=Image.BILINEAR) 

def translate_y(pil_img, level):
  level = int_parameter(sample_level(level), IMAGE_SIZE / 3)
  if np.random.random() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, 0, 0, 1, level),
                           resample=Image.BILINEAR)


# operation that overlaps with ImageNet-C's test set 

def shear_y(pil_img, level):
  level = float_parameter(sample_level(level), 0.3)
  if np.random.uniform() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, 0, level, 1, 0),
                           resample=Image.BILINEAR) 

def shear_x(pil_img, level):
  level = float_parameter(sample_level(level), 0.3)
  if np.random.uniform() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, level, 0, 0, 1, 0),
                           resample=Image.BILINEAR) 

def __getitem__(self, idx):
		for i in range(len(self.acc_count)):
			if idx < self.acc_count[i]:
				label = i
				break

		class_path = self.root_dir + '/' + self.classes[label] 

		if label:
			file_path = class_path + '/' + sorted(os.listdir(class_path))[idx-self.acc_count[label]]
		else:
			file_path = class_path + '/' + sorted(os.listdir(class_path))[idx]

		_, file_name = os.path.split(file_path)

		frames = []

		# print os.listdir(file_path)
		file_list = sorted(os.listdir(file_path))
		# print file_list

		# v: maximum translation in every step
		v = 2
		offset = 0
		for i, f in enumerate(file_list):
			frame = Image.open(file_path + '/' + f)
			#translation
			offset += random.randrange(-v, v)
			offset = min(offset, 3 * v)
			offset = max(offset, -3 * v)
			frame = frame.transform(frame.size, Image.AFFINE, (1, 0, offset, 0, 1, 0))
			if self.transform is not None:
				frame = self.transform[0](frame)
			frames.append(frame)

		return frames, label, file_name